{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import sys \n",
    "from os import getcwd, path\n",
    "sys.path.append(path.dirname(getcwd()))\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [],
   "source": [
    "from utils import data\n",
    "from utils.paper import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from cohorts.styling import set_styling\n",
    "set_styling()\n",
    "from cohorts.functions import *\n",
    "from utils.extra_functions import *\n",
    "from scipy.stats import spearmanr, pearsonr\n",
    "import lifelines as ll\n",
    "import patsy\n",
    "import functools\n",
    "import survivalstan\n",
    "from cohorts.utils import strip_column_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "## set seeds for stan & rngs, to aid in reproducibility\n",
    "## (note: only reproducible within a machine, not across machines)\n",
    "\n",
    "seed = 91038753\n",
    "import random\n",
    "random.seed(seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def update_styling():\n",
    "    import matplotlib as mpl\n",
    "    mpl.rcParams.update({\"legend.fontsize\": 15})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'dataframe_hash': -2899676230513618006,\n",
      " 'provenance_file_summary': {u'cohorts': u'0.4.0+3.gda968fb',\n",
      "                             u'isovar': u'0.0.6',\n",
      "                             u'mhctools': u'0.3.0',\n",
      "                             u'numpy': u'1.11.1',\n",
      "                             u'pandas': u'0.18.1',\n",
      "                             u'pyensembl': u'1.0.3',\n",
      "                             u'scipy': u'0.18.1',\n",
      "                             u'topiary': u'0.1.0',\n",
      "                             u'varcode': u'0.5.10'}}\n",
      "inner join with ensembl_coverage: 25 to 25 rows\n",
      "inner join with tcr_peripheral_a: 25 to 25 rows\n",
      "inner join with tcr_peripheral_a: 25 to 25 rows\n",
      "{'dataframe_hash': -6373961267847645653,\n",
      " 'provenance_file_summary': {u'cohorts': u'0.4.0+3.gda968fb',\n",
      "                             u'isovar': u'0.0.6',\n",
      "                             u'mhctools': u'0.3.0',\n",
      "                             u'numpy': u'1.11.1',\n",
      "                             u'pandas': u'0.18.1',\n",
      "                             u'pyensembl': u'1.0.3',\n",
      "                             u'scipy': u'0.18.1',\n",
      "                             u'topiary': u'0.1.0',\n",
      "                             u'varcode': u'0.5.10'}}\n",
      "inner join with tcr_peripheral_a: 25 to 25 rows\n",
      "inner join with ensembl_coverage: 25 to 25 rows\n",
      "inner join with tcr_tumor: 29 to 24 rows\n",
      "inner join with tcr_tumor: 29 to 24 rows\n",
      "{'dataframe_hash': -2467646966976180658,\n",
      " 'provenance_file_summary': {u'cohorts': u'0.4.0+3.gda968fb',\n",
      "                             u'isovar': u'0.0.6',\n",
      "                             u'mhctools': u'0.3.0',\n",
      "                             u'numpy': u'1.11.1',\n",
      "                             u'pandas': u'0.18.1',\n",
      "                             u'pyensembl': u'1.0.3',\n",
      "                             u'scipy': u'0.18.1',\n",
      "                             u'topiary': u'0.1.0',\n",
      "                             u'varcode': u'0.5.10'}}\n",
      "inner join with tcr_tumor: 29 to 24 rows\n",
      "inner join with tcr_tumor: 29 to 24 rows\n",
      "{{{figure_mv}}}\n"
     ]
    },
    {
     "data": {
      "image/png": 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FQ4ZO7vxXr149AEDPnj2LtXt6eiIpKQkJCQk67Y+IDB8LBpLFd999B0EQMHjw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\nERERycWEgYiIiIiI5Pp/6pEg2jBNUf0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f85776bc790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.gridspec as gridspec\n",
    "\n",
    "fig = plt.figure()\n",
    "gs1 = gridspec.GridSpec(4, 2)\n",
    "ax1 = fig.add_subplot(gs1[0])\n",
    "ax2 = fig.add_subplot(gs1[1])\n",
    "ax3 = fig.add_subplot(gs1[2])\n",
    "ax4 = fig.add_subplot(gs1[3])\n",
    "ax5 = fig.add_subplot(gs1[4:6])\n",
    "ax6 = fig.add_subplot(gs1[6:8])\n",
    "\n",
    "fig.set_size_inches(2250 / 225, 2625 / 225)\n",
    "\n",
    "title_loc = \"left\"\n",
    "title_fontsize = 18\n",
    "title_kwargs = {\"loc\": title_loc, \"fontsize\": title_fontsize, \"fontweight\": \"bold\"}\n",
    "\n",
    "cohort = data.init_cohort()\n",
    "\n",
    "cohort.plot_correlation({\"PD-L1 IC\": lambda row: int(row[\"PD-L1\"].split(\"IC\")[1]),\n",
    "                                    \"# Missense SNVs / MB\": missense_snv_count},\n",
    "                                    plot_type=\"boxplot\",\n",
    "                                    x_col=\"PD-L1 IC\",\n",
    "                                    plot_kwargs={\"ax\": ax1})\n",
    "ax1.set_title(\"A\", **title_kwargs)\n",
    "\n",
    "cohort = data.init_cohort(join_with=\"tcr_peripheral_a\")\n",
    "cohort.tcr_peripheral_a_clonality_plot_name = \"Pre-tx Peripheral TCR Clon.\"\n",
    "\n",
    "results = cohort.plot_correlation({cohort.tcr_peripheral_a_clonality_plot_name: lambda row: \n",
    "                                                 row[\"Clonality\"],\n",
    "                                    \"# Missense SNVs / MB\": missense_snv_count},\n",
    "                                    plot_type=\"regplot\",\n",
    "                                    x_col=cohort.tcr_peripheral_a_clonality_plot_name,\n",
    "                                    plot_kwargs={\"ax\": ax2,\n",
    "                                                 \"fit_reg\": False})\n",
    "ax2.set_title(\"B\", **title_kwargs)\n",
    "results.plot.set_xlim((0, 0.4))\n",
    "results.plot.set_ylim((0, 12))\n",
    "\n",
    "cohort = data.init_cohort(join_with=\"tcr_tumor\", \n",
    "                          exclude_patient_ids=set(),\n",
    "                          only_patients_with_bams=False)\n",
    "\n",
    "result = cohort.plot_correlation({\"PD-L1 IC\": lambda row: int(row[\"PD-L1\"].split(\"IC\")[1]),\n",
    "                                    \"TIL Proportion\": \n",
    "                                      lambda row: row[\"T-cell fraction\"]},\n",
    "                                    plot_type=\"boxplot\",\n",
    "                                    x_col=\"PD-L1 IC\",\n",
    "                                    stat_func=spearmanr,\n",
    "                                    plot_kwargs={\"ax\": ax3})\n",
    "result.plot.set_xticklabels([\"0\", \"1\", \"2\"])\n",
    "ax3.set_title(\"C\", **title_kwargs)\n",
    "\n",
    "result = cohort.plot_correlation({\"PD-L1 IC\": lambda row: int(row[\"PD-L1\"].split(\"IC\")[1]),\n",
    "                                    \"TIL Clonality\": \n",
    "                                      lambda row: row[\"Clonality\"]},\n",
    "                                    plot_type=\"boxplot\",\n",
    "                                    x_col=\"PD-L1 IC\",\n",
    "                                    stat_func=spearmanr,\n",
    "                                    plot_kwargs={\"ax\": ax4})\n",
    "result.plot.set_xticklabels([\"0\", \"1\", \"2\"])\n",
    "ax4.set_title(\"D\", **title_kwargs)\n",
    "\n",
    "if \"tcrclonality_by_pdl1\" not in globals():\n",
    "    def get_df_longs():\n",
    "        cohort = data.init_cohort(join_with=[\"ensembl_coverage\",\"tcr_peripheral_a\"])\n",
    "\n",
    "        def tcell_fraction(row):\n",
    "            return row[\"T-cell fraction\"]\n",
    "\n",
    "        def peripheral_clonality_a(row):\n",
    "            return row['Clonality']\n",
    "\n",
    "        cols, d = cohort.as_dataframe([snv_count,\n",
    "                                       missense_snv_count,\n",
    "                                       neoantigen_count,\n",
    "                                       expressed_exonic_snv_count,\n",
    "                                       expressed_missense_snv_count,\n",
    "                                       expressed_neoantigen_count,\n",
    "                                       exonic_snv_count,\n",
    "                                       peripheral_clonality_a,\n",
    "                                       tcell_fraction,\n",
    "                                       ],\n",
    "                                      rename_cols=True,\n",
    "                                      return_cols=True)\n",
    "\n",
    "        ## add/modify count variables\n",
    "        d['nonexonic_snv_count'] = d.snv_count - d.exonic_snv_count\n",
    "        cols.append('nonexonic_snv_count')\n",
    "\n",
    "        ## create 'observed', log-transformed & centered versions of variables (not normalized by MB)\n",
    "        for col in cols:\n",
    "            observed_col = 'observed_{}'.format(col)\n",
    "            log_col = 'log_{}'.format(col)\n",
    "            log_col_centered = 'log_{}_centered'.format(col)\n",
    "            log_col_rescaled = 'log_{}_rescaled'.format(col)\n",
    "            d[observed_col] = d[col]*d['mb']\n",
    "            d[log_col] = np.log1p(d[observed_col])\n",
    "            d[log_col_centered] = d[log_col] - np.mean(d[log_col])\n",
    "            d[log_col_rescaled] = d[log_col_centered]/np.std(d[log_col_centered])\n",
    "\n",
    "        ## save key vars in list for future use\n",
    "        vars_centered = ['log_{}_centered'.format(col) for col in cols]\n",
    "        vars_rescaled = ['log_{}_rescaled'.format(col) for col in cols]\n",
    "\n",
    "        ## construct new variables for key ratios / comparisons\n",
    "\n",
    "        # what proportion of X are expressed?\n",
    "        d['exonic_expression_ratio'] = d.expressed_exonic_snv_count / d.exonic_snv_count\n",
    "        d['missense_expression_ratio'] = d.expressed_missense_snv_count / d.missense_snv_count\n",
    "        d['neoantigen_expression_ratio'] = d.expressed_neoantigen_count / d.neoantigen_count\n",
    "\n",
    "        # d['expressed_missense2snv_ratio'] = d.expressed_missense_snv_count / d.expressed_snv_count\n",
    "        d['missense2exonic_snv_ratio'] = d.missense_snv_count / d.exonic_snv_count\n",
    "        d['expressed_neoantigen2missense_ratio'] = d.expressed_neoantigen_count / d.expressed_missense_snv_count\n",
    "\n",
    "        extra_cols = ['missense_expression_ratio','neoantigen_expression_ratio', 'exonic_expression_ratio', 'missense2exonic_snv_ratio','expressed_neoantigen2missense_ratio']\n",
    "        ## create recentered versions of ratios\n",
    "        for col in extra_cols:\n",
    "            col_centered = '{}_centered'.format(col)\n",
    "            col_rescaled = '{}_rescaled'.format(col)\n",
    "            d[col_centered] = d[col] - np.mean(d[col])\n",
    "            d[col_rescaled] = d[col_centered]/np.std(d[col_centered])\n",
    "\n",
    "        ## append extra-cols to key var lists\n",
    "        vars_centered.extend(['{}_centered'.format(col) for col in extra_cols])\n",
    "        vars_rescaled.extend(['{}_rescaled'.format(col) for col in extra_cols])\n",
    "\n",
    "        ## identify list of variables to center\n",
    "        metrics = list(cols)\n",
    "        metrics.extend(extra_cols)\n",
    "\n",
    "        metrics2 = list(metrics)\n",
    "        metrics2.extend(['pd_l1'])\n",
    "        assert(not 'pd_l1' in metrics)\n",
    "        log_metrics2 = ['log_{}'.format(var) for var in metrics2]\n",
    "        metrics2.extend(log_metrics2)\n",
    "\n",
    "        grp_metrics = [var for var in metrics2 if var in d.columns]\n",
    "\n",
    "        # center variables by group\n",
    "        bygrp = d.loc[:, grp_metrics]\n",
    "        bygrp = bygrp.groupby('pd_l1').transform(lambda x: x - x.mean())\n",
    "        bygrp['patient_id'] = d.patient_id\n",
    "\n",
    "        # merge recentered variables back into original dataframe\n",
    "        df = pd.merge(d, bygrp, on = 'patient_id', suffixes = ['', '_centered_by_pd_l1'])\n",
    "\n",
    "        ## prep dflong_pfs which will be used for survival analysis using stan\n",
    "        df_long_pfs = survivalstan.prep_data_long_surv(df = df,\n",
    "                                                       event_col = 'is_progressed_or_deceased',\n",
    "                                                       time_col = 'pfs')\n",
    "        df_long_os = survivalstan.prep_data_long_surv(df = df,\n",
    "                                                       event_col = 'is_deceased',\n",
    "                                                       time_col = 'os')\n",
    "        return df_long_pfs, df_long_os\n",
    "\n",
    "    df_long_pfs, df_long_os = get_df_longs()\n",
    "\n",
    "    models = survivalstan.utils.read_files('../utils/stan')\n",
    "\n",
    "    survstan_pfs_varcoef = functools.partial(\n",
    "        survivalstan.fit_stan_survival_model,\n",
    "        df = df_long_pfs,\n",
    "        model_code = models['pem_survival_model_unstructured_varcoef.stan'],\n",
    "        timepoint_end_col = 'end_time',\n",
    "        event_col = 'end_failure',\n",
    "        sample_col = 'patient_id',\n",
    "        chains = 4,\n",
    "        iter = 10000,\n",
    "        grp_coef_type = 'vector-of-vectors',\n",
    "        seed = seed,\n",
    "        )\n",
    "    survstan_os_varcoef = functools.partial(\n",
    "        survivalstan.fit_stan_survival_model,\n",
    "        df = df_long_os,\n",
    "        model_code = models['pem_survival_model_unstructured_varcoef.stan'],\n",
    "        timepoint_end_col = 'end_time',\n",
    "        event_col = 'end_failure',\n",
    "        sample_col = 'patient_id',\n",
    "        chains = 4,\n",
    "        iter = 10000,\n",
    "        grp_coef_type = 'vector-of-vectors',\n",
    "        seed = seed,\n",
    "        )\n",
    "\n",
    "    tcrclonality_by_pdl1 = survstan_pfs_varcoef(formula = 'log_peripheral_clonality_a_centered_by_pd_l1', \n",
    "                                   group_col = 'pd_l1',\n",
    "                                  )\n",
    "\n",
    "result = sb.boxplot(ax=ax5, \n",
    "           data=tcrclonality_by_pdl1['grp_coefs'], x='exp(beta)', y='group', fliersize=0, whis=[2.5, 97.5])\n",
    "_ = result.axes.set_xlim([0, 30])\n",
    "_ = result.axes.set_yticklabels([\"0\", \"1\", \"2\"])\n",
    "_ = result.axes.vlines(1, -10, 10, linestyles='--')\n",
    "_ = result.axes.set_ylabel(\"PD-L1 IC\")\n",
    "_ = result.axes.set_xlabel('HR for {}'.format(cohort.hazard_plot_name))\n",
    "ax5.set_title(\"E\", **title_kwargs)\n",
    "\n",
    "if \"all_coefs\" not in globals():\n",
    "    formula = 'liver_mets + {}_centered_by_pd_l1 + {}_centered_by_pd_l1 + {}_centered_by_pd_l1'.format(\n",
    "        'log_missense_snv_count',\n",
    "         'log_peripheral_clonality_a',\n",
    "         'log_tcell_fraction',\n",
    "        )\n",
    "    multivariate_models_varcoef = list()\n",
    "    multivariate_models_varcoef.append(survstan_pfs_varcoef(formula = formula,\n",
    "                                                            model_cohort = formula,\n",
    "                                                            group_col='pd_l1'))\n",
    "\n",
    "    ## prep 'beta' coefficients for IC groups\n",
    "    ## so that this can be communicated as well\n",
    "    grp_alpha = survivalstan.utils.extract_params_long([multivariate_models_varcoef[0]],\n",
    "                                                       element='grp_alpha',\n",
    "                                                       varnames = ['IC0','IC1','IC2'])\n",
    "    ic0_values = grp_alpha.loc[grp_alpha['variable']=='IC0',['iter','value','model_cohort']]\n",
    "    ic0_values.rename(columns={'value': 'ic0_value'}, inplace=True)\n",
    "    grp_alpha = grp_alpha.merge(ic0_values, on=['iter','model_cohort'])\n",
    "    grp_alpha['beta'] = grp_alpha.apply(lambda row: row['value'] - row['ic0_value'], axis=1)\n",
    "    grp_alpha['exp(beta)'] = np.exp(grp_alpha['beta'])\n",
    "    grp_alpha.drop('value', axis=1, inplace=True)\n",
    "    grp_alpha.rename(columns = {'variable': 'group', 'beta': 'value'}, inplace=True)\n",
    "    grp_alpha['variable'] = 'intercept'\n",
    "\n",
    "    all_coefs = pd.concat([grp_alpha, multivariate_models_varcoef[0]['grp_coefs']])\n",
    "\n",
    "    all_coefs['PD-L1 IC'] = all_coefs['group'].apply(lambda val: int(val[-1]))\n",
    "\n",
    "update_styling()\n",
    "\n",
    "result = sb.boxplot(ax=ax6,\n",
    "           data = all_coefs,\n",
    "           x = 'exp(beta)',\n",
    "           y = 'variable',\n",
    "           hue = 'PD-L1 IC',\n",
    "           fliersize = 0,\n",
    "           whis = [2.5, 97.5],\n",
    "          )\n",
    "_ = result.axes.set_xlim([0, 10])\n",
    "_ = result.axes.set_ylabel('')\n",
    "_ = result.axes.vlines(1, -10, 10, linestyles='--')\n",
    "tcr_clonality_plot_name = \"Pre-tx Peripheral TCR Clon.\"\n",
    "tcr_c_p1 = \" \".join(tcr_clonality_plot_name.split(\" \")[:2])\n",
    "tcr_c_p2 = \" \".join(tcr_clonality_plot_name.split(\" \")[2:])\n",
    "tcr_clonality_plot_name = \"\\n\".join([tcr_c_p1, tcr_c_p2])\n",
    "_ = result.axes.set_yticklabels(\n",
    "               ['Intercept (PD-L1 Effect)',\n",
    "                'Liver Metastasis',\n",
    "                'log(# Missense\\nSNVs / MB)',\n",
    "                'log({tcr_plot_name})'.format(tcr_plot_name=tcr_clonality_plot_name),\n",
    "                'log({til_fraction_plot_name})'.format(til_fraction_plot_name=cohort.til_fraction_plot_name),\n",
    "                ])\n",
    "_ = result.axes.set_xlabel('HR for {}'.format(cohort.hazard_plot_name))\n",
    "ax6.set_title(\"F\", **title_kwargs)\n",
    "ax6.legend(loc=\"lower right\", title=\"PD-L1 IC\", ncol=3, prop={'size':15})\n",
    "\n",
    "plt.subplots_adjust(bottom=-0.5)\n",
    "plt.subplots_adjust(wspace=0.3)\n",
    "plt.subplots_adjust(hspace=0.3)\n",
    "\n",
    "hyper_figure_label_printer(\"figure_mv\")\n",
    "\n",
    "fig.savefig(path.join(data.REPO_DATA_DIR, 'Fig4.tif'), format='tif', dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from utils.paper import resize_image_plos\n",
    "resize_image_plos(\"Fig4.tif\", \"Fig4.tif\", figure_path=data.REPO_DATA_DIR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python [conda env:bladder2]",
   "language": "python",
   "name": "conda-env-bladder2-py"
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   "codemirror_mode": {
    "name": "ipython",
    "version": 2
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
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  "name": "TCR Clonality.ipynb"
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